{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e564ae1e-bd26-4e3b-a18a-c1bf6bdb95ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sys\n",
    "import os\n",
    "sys.path.append('../../src')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f7f2c72d-69f4-45c9-89cb-a8cd6d689736",
   "metadata": {},
   "outputs": [],
   "source": [
    "from one_dragon.yolo.devtools import os_utils\n",
    "from one_dragon.yolo.devtools import ultralytics_utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "49e57432-67df-4778-9b33-87b2132346c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "ultralytics_utils.init_ultralytics_settings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cfe8bc76-9d7e-4f61-abe6-74d27e5e4363",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ultralytics import YOLO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "51ea149c-6f9c-4126-96ab-6bc3867fda47",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = YOLO(ultralytics_utils.get_base_model_path('yolov8n-cls.pt'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e4b91840-d2e4-4a30-9826-768aef87dd28",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_name = 'zzz-dodge-raw'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9010a42e-3ed0-4e99-972c-168d5b8144e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "imgsz=(540, 960)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e5d1f7f3-314a-46dc-b6fe-633e0beb5e66",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ultralytics YOLOv8.2.51  Python-3.11.9 torch-2.3.1 CUDA:0 (NVIDIA GeForce RTX 4070 Ti, 12282MiB)\n",
      "\u001b[34m\u001b[1mengine\\trainer: \u001b[0mtask=classify, mode=train, model=D:\\code\\workspace\\OneDragon-YOLO\\ultralytics\\models\\yolov8n-cls.pt, data=D:\\code\\workspace\\OneDragon-YOLO\\ultralytics\\datasets\\zzz-dodge-raw, epochs=200, time=None, patience=100, batch=-1, imgsz=(540, 960), save=True, save_period=-1, cache=False, device=None, workers=8, project=D:\\code\\workspace\\OneDragon-YOLO\\ultralytics\\runs\\zzz-dodge-raw, name=train7, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=None, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=D:\\code\\workspace\\OneDragon-YOLO\\ultralytics\\runs\\zzz-dodge-raw\\train7\n",
      "\u001b[34m\u001b[1mtrain:\u001b[0m D:\\code\\workspace\\OneDragon-YOLO\\ultralytics\\datasets\\zzz-dodge-raw\\train... found 507 images in 3 classes  \n",
      "\u001b[34m\u001b[1mval:\u001b[0m D:\\code\\workspace\\OneDragon-YOLO\\ultralytics\\datasets\\zzz-dodge-raw\\val... found 90 images in 4 classes: ERROR  requires 3 classes, not 4\n",
      "\u001b[34m\u001b[1mtest:\u001b[0m None...\n",
      "Overriding model.yaml nc=1000 with nc=3\n",
      "\n",
      "                   from  n    params  module                                       arguments                     \n",
      "  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 \n",
      "  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                \n",
      "  2                  -1  1      7360  ultralytics.nn.modules.block.C2f             [32, 32, 1, True]             \n",
      "  3                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                \n",
      "  4                  -1  2     49664  ultralytics.nn.modules.block.C2f             [64, 64, 2, True]             \n",
      "  5                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               \n",
      "  6                  -1  2    197632  ultralytics.nn.modules.block.C2f             [128, 128, 2, True]           \n",
      "  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              \n",
      "  8                  -1  1    460288  ultralytics.nn.modules.block.C2f             [256, 256, 1, True]           \n",
      "  9                  -1  1    334083  ultralytics.nn.modules.head.Classify         [256, 3]                      \n",
      "YOLOv8n-cls summary: 99 layers, 1442131 parameters, 1442131 gradients\n",
      "Transferred 156/158 items from pretrained weights\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLOv8n...\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed \n",
      "WARNING  updating to 'imgsz=960'. 'train' and 'val' imgsz must be an integer, while 'predict' and 'export' imgsz may be a [h, w] list or an integer, i.e. 'yolo export imgsz=640,480' or 'yolo export imgsz=640'\n",
      "\u001b[34m\u001b[1mAutoBatch: \u001b[0mComputing optimal batch size for imgsz=960 at 60.0% CUDA memory utilization.\n",
      "\u001b[34m\u001b[1mAutoBatch: \u001b[0mCUDA:0 (NVIDIA GeForce RTX 4070 Ti) 11.99G total, 1.03G reserved, 0.11G allocated, 10.86G free\n",
      "      Params      GFLOPs  GPU_mem (GB)  forward (ms) backward (ms)                   input                  output\n",
      "     1442131           0         1.139         27.66         46.16        (1, 3, 960, 960)                  (1, 3)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x000002418612EAC0>\n",
      "Traceback (most recent call last):\n",
      "  File \"D:\\code\\env\\anaconda\\envs\\mhy-yolo\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py\", line 1479, in __del__\n",
      "    self._shutdown_workers()\n",
      "  File \"D:\\code\\env\\anaconda\\envs\\mhy-yolo\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py\", line 1443, in _shutdown_workers\n",
      "    w.join(timeout=_utils.MP_STATUS_CHECK_INTERVAL)\n",
      "  File \"D:\\code\\env\\anaconda\\envs\\mhy-yolo\\Lib\\multiprocessing\\process.py\", line 149, in join\n",
      "    res = self._popen.wait(timeout)\n",
      "          ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"D:\\code\\env\\anaconda\\envs\\mhy-yolo\\Lib\\multiprocessing\\popen_spawn_win32.py\", line 112, in wait\n",
      "    res = _winapi.WaitForSingleObject(int(self._handle), msecs)\n",
      "          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "KeyboardInterrupt: \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     1442131           0         0.684         35.37         64.51        (2, 3, 960, 960)                  (2, 3)\n",
      "     1442131           0         1.088         22.02         27.36        (4, 3, 960, 960)                  (4, 3)\n",
      "     1442131           0         1.839         24.69         35.92        (8, 3, 960, 960)                  (8, 3)\n",
      "     1442131           0         3.595         33.18         51.94       (16, 3, 960, 960)                 (16, 3)\n",
      "\u001b[34m\u001b[1mAutoBatch: \u001b[0mUsing batch-size 32 for CUDA:0 7.55G/11.99G (63%) \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning D:\\code\\workspace\\OneDragon-YOLO\\ultralytics\\datasets\\zzz-dodge-raw\\train... 507 images, 0 corrupt: 100%|██████████| 507/507 [00:00<?, ?it/s]\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning D:\\code\\workspace\\OneDragon-YOLO\\ultralytics\\datasets\\zzz-dodge-raw\\val... 90 images, 0 corrupt: 100%|██████████| 90/90 [00:00<?, ?it/s]\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.000714, momentum=0.9) with parameter groups 26 weight(decay=0.0), 27 weight(decay=0.0005), 27 bias(decay=0.0)\n",
      "Image sizes 960 train, 960 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mD:\\code\\workspace\\OneDragon-YOLO\\ultralytics\\runs\\zzz-dodge-raw\\train7\u001b[0m\n",
      "Starting training for 200 epochs...\n",
      "\n",
      "      Epoch    GPU_mem       loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      1/200      7.28G      1.004         27        960: 100%|██████████| 16/16 [00:17<00:00,  1.07s/it]\n",
      "               classes   top1_acc   top5_acc: 100%|██████████| 2/2 [00:01<00:00,  1.78it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all      0.622          1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem       loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      2/200      6.48G     0.8644         27        960: 100%|██████████| 16/16 [00:08<00:00,  1.86it/s]\n",
      "               classes   top1_acc   top5_acc: 100%|██████████| 2/2 [00:00<00:00,  6.14it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all      0.778          1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem       loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      3/200      6.48G     0.7536         27        960: 100%|██████████| 16/16 [00:08<00:00,  1.97it/s]\n",
      "               classes   top1_acc   top5_acc: 100%|██████████| 2/2 [00:00<00:00,  6.64it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all      0.822          1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem       loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      4/200      6.48G     0.6067         27        960: 100%|██████████| 16/16 [00:08<00:00,  1.83it/s]\n",
      "               classes   top1_acc   top5_acc: 100%|██████████| 2/2 [00:00<00:00,  7.09it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all      0.867          1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem       loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      5/200      6.48G     0.4087         27        960: 100%|██████████| 16/16 [00:09<00:00,  1.76it/s]\n",
      "               classes   top1_acc   top5_acc: 100%|██████████| 2/2 [00:00<00:00,  6.80it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all      0.889          1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem       loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      6/200      6.48G     0.2535         27        960: 100%|██████████| 16/16 [00:08<00:00,  1.88it/s]\n",
      "               classes   top1_acc   top5_acc: 100%|██████████| 2/2 [00:00<00:00,  6.89it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all      0.989          1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem       loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      7/200      6.48G     0.1644         27        960: 100%|██████████| 16/16 [00:08<00:00,  1.86it/s]\n",
      "               classes   top1_acc   top5_acc: 100%|██████████| 2/2 [00:00<00:00,  7.09it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all      0.978          1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem       loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      8/200      6.48G     0.1043         27        960: 100%|██████████| 16/16 [00:08<00:00,  1.90it/s]\n",
      "               classes   top1_acc   top5_acc: 100%|██████████| 2/2 [00:00<00:00,  6.77it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all      0.978          1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem       loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      9/200      6.48G    0.06198         27        960: 100%|██████████| 16/16 [00:08<00:00,  1.86it/s]\n",
      "               classes   top1_acc   top5_acc: 100%|██████████| 2/2 [00:00<00:00,  6.87it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all          1          1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem       loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     10/200      6.48G    0.07093         27        960: 100%|██████████| 16/16 [00:08<00:00,  1.79it/s]\n",
      "               classes   top1_acc   top5_acc: 100%|██████████| 2/2 [00:00<00:00,  6.89it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all      0.967          1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem       loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     11/200      6.48G    0.09545         27        960: 100%|██████████| 16/16 [00:08<00:00,  1.84it/s]\n",
      "               classes   top1_acc   top5_acc: 100%|██████████| 2/2 [00:00<00:00,  6.91it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all          1          1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem       loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     12/200      6.48G    0.07581         27        960: 100%|██████████| 16/16 [00:08<00:00,  1.79it/s]\n",
      "               classes   top1_acc   top5_acc: 100%|██████████| 2/2 [00:00<00:00,  6.93it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all          1          1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem       loss  Instances       Size\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "     13/200      6.48G    0.05876         27        960: 100%|██████████| 16/16 [00:08<00:00,  1.85it/s]\n",
      "               classes   top1_acc   top5_acc: 100%|██████████| 2/2 [00:00<00:00,  6.93it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all          1          1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem       loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     14/200      6.48G    0.04435         27        960: 100%|██████████| 16/16 [00:08<00:00,  1.80it/s]\n",
      "               classes   top1_acc   top5_acc: 100%|██████████| 2/2 [00:00<00:00,  6.89it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all      0.989          1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     "text": [
      "\n",
      "200 epochs completed in 0.539 hours.\n",
      "Optimizer stripped from D:\\code\\workspace\\OneDragon-YOLO\\ultralytics\\runs\\zzz-dodge-raw\\train7\\weights\\last.pt, 3.0MB\n",
      "Optimizer stripped from D:\\code\\workspace\\OneDragon-YOLO\\ultralytics\\runs\\zzz-dodge-raw\\train7\\weights\\best.pt, 3.0MB\n",
      "\n",
      "Validating D:\\code\\workspace\\OneDragon-YOLO\\ultralytics\\runs\\zzz-dodge-raw\\train7\\weights\\best.pt...\n",
      "Ultralytics YOLOv8.2.51  Python-3.11.9 torch-2.3.1 CUDA:0 (NVIDIA GeForce RTX 4070 Ti, 12282MiB)\n",
      "YOLOv8n-cls summary (fused): 73 layers, 1438723 parameters, 0 gradients\n",
      "\u001b[34m\u001b[1mtrain:\u001b[0m D:\\code\\workspace\\OneDragon-YOLO\\ultralytics\\datasets\\zzz-dodge-raw\\train... found 507 images in 3 classes  \n",
      "\u001b[34m\u001b[1mval:\u001b[0m D:\\code\\workspace\\OneDragon-YOLO\\ultralytics\\datasets\\zzz-dodge-raw\\val... found 90 images in 4 classes: ERROR  requires 3 classes, not 4\n",
      "\u001b[34m\u001b[1mtest:\u001b[0m None...\n"
     ]
    },
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    },
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      "                   all          1          1\n",
      "Speed: 2.3ms preprocess, 0.9ms inference, 0.0ms loss, 0.0ms postprocess per image\n",
      "Results saved to \u001b[1mD:\\code\\workspace\\OneDragon-YOLO\\ultralytics\\runs\\zzz-dodge-raw\\train7\u001b[0m\n",
      "Results saved to \u001b[1mD:\\code\\workspace\\OneDragon-YOLO\\ultralytics\\runs\\zzz-dodge-raw\\train7\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ultralytics.utils.metrics.ClassifyMetrics object with attributes:\n",
       "\n",
       "confusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x00000241A53EF8D0>\n",
       "curves: []\n",
       "curves_results: []\n",
       "fitness: 1.0\n",
       "keys: ['metrics/accuracy_top1', 'metrics/accuracy_top5']\n",
       "results_dict: {'metrics/accuracy_top1': 1.0, 'metrics/accuracy_top5': 1.0, 'fitness': 1.0}\n",
       "save_dir: WindowsPath('D:/code/workspace/OneDragon-YOLO/ultralytics/runs/zzz-dodge-raw/train7')\n",
       "speed: {'preprocess': 2.347678608364529, 'inference': 0.9341637293497721, 'loss': 0.0, 'postprocess': 0.0}\n",
       "task: 'classify'\n",
       "top1: 1.0\n",
       "top5: 1.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.train(\n",
    "    data=ultralytics_utils.get_dataset_dir(dataset_name),  # 数据集配置文件的位置\n",
    "    project=ultralytics_utils.get_dataset_model_dir(dataset_name),  # 训练模型的数据（包括模型文件）的自动保存位置\n",
    "    epochs=200,\n",
    "    imgsz=imgsz,\n",
    "    batch=-1,  # 根据可使用内存 自动判断batch_size\n",
    "    auto_augment=None,  # 关闭旋转, 倾斜等变换。闪避的红黄光是很正的十字，避免跟其他的闪关混淆\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e7d84a53-ea2d-481d-af2e-94a84b2d3b77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ultralytics YOLOv8.2.51  Python-3.11.9 torch-2.3.1 CPU (AMD Ryzen 9 5950X 16-Core Processor)\n",
      "YOLOv8n-cls summary (fused): 73 layers, 1438723 parameters, 0 gradients\n",
      "\n",
      "\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from 'D:\\code\\workspace\\OneDragon-YOLO\\ultralytics\\runs\\zzz-dodge-raw\\train6\\weights\\best.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 3) (2.8 MB)\n",
      "\n",
      "\u001b[34m\u001b[1mONNX:\u001b[0m starting export with onnx 1.16.0 opset 17...\n",
      "\u001b[34m\u001b[1mONNX:\u001b[0m export success  0.3s, saved as 'D:\\code\\workspace\\OneDragon-YOLO\\ultralytics\\runs\\zzz-dodge-raw\\train6\\weights\\best.onnx' (5.5 MB)\n",
      "\n",
      "Export complete (2.2s)\n",
      "Results saved to \u001b[1mD:\\code\\workspace\\OneDragon-YOLO\\ultralytics\\runs\\zzz-dodge-raw\\train6\\weights\u001b[0m\n",
      "Predict:         yolo predict task=classify model=D:\\code\\workspace\\OneDragon-YOLO\\ultralytics\\runs\\zzz-dodge-raw\\train6\\weights\\best.onnx imgsz=640  \n",
      "Validate:        yolo val task=classify model=D:\\code\\workspace\\OneDragon-YOLO\\ultralytics\\runs\\zzz-dodge-raw\\train6\\weights\\best.onnx imgsz=640 data=D:\\code\\workspace\\OneDragon-YOLO\\ultralytics\\datasets\\zzz-dodge-raw  \n",
      "Visualize:       https://netron.app\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'D:\\\\code\\\\workspace\\\\OneDragon-YOLO\\\\ultralytics\\\\runs\\\\zzz-dodge-raw\\\\train6\\\\weights\\\\best.onnx'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.export(format=\"onnx\", imgsz=640)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8f84224d-f07e-4084-a7d8-ffb2d411697b",
   "metadata": {},
   "outputs": [],
   "source": [
    "val_dir = os.path.join(ultralytics_utils.get_dataset_dir(dataset_name), 'val')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bac68da3-f49e-46eb-a0f3-ce500c79316f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "574bad92-7bf4-4daf-91ee-3e97c451aa80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "image 1/1 D:\\code\\workspace\\OneDragon-YOLO\\ultralytics\\datasets\\zzz-dodge-raw\\val\\02-yellow\\switch_1720366078771.png: 640x640 02-yellow 1.00, 01-red 0.00, 00-none 0.00, 6.0ms\n",
      "Speed: 25.0ms preprocess, 6.0ms inference, 0.0ms postprocess per image at shape (1, 3, 640, 640)\n"
     ]
    }
   ],
   "source": [
    "class_dir = os.path.join(\n",
    "    val_dir,\n",
    "    random.choice(os.listdir(val_dir))\n",
    ")\n",
    "image = os.path.join(\n",
    "    class_dir,\n",
    "    random.choice(os.listdir(class_dir))\n",
    ")\n",
    "results = model(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2ad81cc0-7beb-45ad-a222-60a34ce801e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from zzz_od.yolo.switch_classifier import SwitchClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c938c9d3-0525-488f-aa7d-e9215acfbbe0",
   "metadata": {},
   "outputs": [],
   "source": [
    "SwitchClassifier"
   ]
  }
 ],
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